PRICE = + SQFT + AGE + BEDS + BATHS + u. Jack\'s house has a living area of 1800

Question

PRICE = + SQFT + AGE + BEDS + BATHS + u. Jack's house has a living area of 1800 sqft. He is planning to add another bedroom of size 200 sqft. Build a 95% interval estimate for the expected price increase.

PRICE SQFT AGE BEDS BATHS 138000 1700 97 3 2 105700 2100 18 4 2.5 22000 700 49 2 1 255000 3000 23 3 3 203000 2100 18 4 2 129178 1600 2 3 2 140000 1700 0 3 2 118600 1400 4 3 2 125000 1400 3 3 2 145000 1700 1 3 2.5 157000 1700 3 3 2.5 160000 1900 4 4 2.5 151000 1700 0 3 2.5 166000 1900 0 4 2 124500 1400 1 3 2 145000 1400 3 3 2 144000 1600 0 4 2 175000 1800 3 4 2 125000 1400 0 3 2 172500 2200 2 4 2.5 330000 3500 0 4 3.5 233900 2400 0 4 2 199950 2100 0 3 3 159000 1900 7 4 2 180000 2600 5 4 3 155000 2300 6 4 2.5 129000 1600 6 3 2 149900 2700 6 4 3 235000 3300 24 5 3.5 137000 1700 29 3 2 219000 2100 23 3 2 210000 2100 22 3 3 190000 2200 22 2 2.5 247000 2500 24 4 3 125000 1600 3 3 2 199000 2600 4 4 3 132500 1700 4 4 2 161000 2100 2 4 2.5 166300 2100 4 4 2.5 146500 2300 1 4 3 180000 2600 3 4 3 142000 1900 4 3 2 149950 1800 4 4 2 128000 1300 4 3 2 127000 1300 4 3 2 128000 1500 5 3 2 134000 1400 4 3 2 134000 1500 6 3 2 136500 1500 6 3 2 130000 1300 4 3 2 186500 2100 6 5 3 168500 2100 6 5 3 169950 1800 2 3 2 179900 2000 0 4 3 146500 2000 3 4 2 104500 1600 16 3 2 154000 2100 0 4 2.5 110000 1200 14 3 2 100000 1000 13 2 2 155000 1600 13 3 2 161900 1900 12 3 2.5 169000 1700 14 3 2 134000 1700 13 2 2 145000 1900 8 3 2.5 132500 1800 12 3 2.5 131000 1900 10 3 2.5 147000 2100 9 5 3 190000 2100 9 4 2.5 147500 2100 8 3 2.5 175000 2200 8 4 2 188000 2700 8 3 3 172000 2700 7 4 3 163000 1800 8 3 2 209900 2700 8 4 3 163000 1900 8 3 2 179000 2700 8 3 3 149000 1800 8 3 2 121000 1800 3 4 2 134900 1800 5 4 2 160000 1800 8 3 2 164000 2500 1 4 2 148500 1800 3 4 2.5 100000 1600 6 3 2 107000 1600 6 3 2 111000 1600 6 3 2 100000 1600 4 3 2 148000 1600 6 3 2 127500 1800 6 4 3 136500 2000 7 4 2.5 160000 2500 2 4 3 152500 2100 3 4 2.5 132000 1600 3 3 2 126500 1600 3 3 2 149750 1800 7 3 2 165000 2400 7 4 2.5 137500 2000 7 4 2.5 145000 1900 8 4 2 136000 1800 7 3 2 156500 2400 6 4 2.5

Explanation / Answer

we shall use R to answer this

# read the data into R dataframe
data.df<- read.csv("C:\Users\586645\Downloads\Chegg\house.csv",header=TRUE)
str(data.df)

## regression

fit <- lm(PRICE~., data = data.df)
summary(fit)

## jacks case

newdat <- data.frame(SQFT= 2000,AGE=90,BEDS=4,BATHS=4)

## USE PREDICT FUNCTION

predict(fit,newdat,interval="prediction")

The results are

Hence the regression equation is formed using the coefficients as

Price = 36964 +70.33sqgt -86.33*age -5733.2*beds +868baths

> summary(fit)

Call:
lm(formula = PRICE ~ ., data = data.df)

Residuals:
Min 1Q Median 3Q Max
-56645 -15106 1592 13486 66767

Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 36964.015 15469.029 2.390 0.0189 *
SQFT 70.332 8.925 7.880 5.69e-12 ***
AGE -86.331 208.741 -0.414 0.6801
BEDS -5733.289 4615.221 -1.242 0.2172
BATHS 868.066 8846.214 0.098 0.9220
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 23440 on 94 degrees of freedom
Multiple R-squared: 0.6434,   Adjusted R-squared: 0.6282
F-statistic: 42.4 on 4 and 94 DF, p-value: < 2.2e-16

data for baths , beds and age is not given so I shall assume some values

> predict(fit,newdat,interval="prediction")
fit lwr upr
1 150398.3 84350.3 216446.2

Related Questions

Navigate

• Browse (All)
• Browse P
• Subjects

---

---

Integrity-first tutoring: explanations and feedback only — we do not complete graded work. Learn more.